alexa Brain Signal Analysis For Emotion Recognition And Brain Machine Interface | 31536
ISSN: 2165- 7866

Journal of Information Technology & Software Engineering
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Global Summit and Expo on Multimedia & Applications

Vijayan K Asari
Keynote: J Inform Tech Soft Engg
DOI: 10.4172/2165-7866.S1.001
Abstract

Emotion recognition by analyzing electroencephalographic (EEG) recordings is a growing area of research. EEG can detect
neurological activities and collect data representing brain signals without the need for any invasive technology or procedures.
EEG recordings are found useful for the detection of emotions through monitoring the characteristics of spatiotemporal variations
of activations inside the brain. Specific spectral descriptors as features are extracted from EEG data to quantify the spatiotemporal
variations to distinguish different emotions. Several features representing different brain activities are estimated for the classification
of emotions. A brain machine interface using EEG data facilitates the control of machines through the analysis and classification
of signals directly from the human brain. The collected EEG data is analyzed by an independent component analysis based feature
extraction methodology and classified using a multilayer neural network classifier into several control signals for controlling a
robot. The system also collects the data of electromyography signals indicative of movement of the facial muscles. Research work
is progressing to extend the range of controls beyond a set of discrete actions by refining the algorithmic steps and procedures.

Biography

Vijayan K Asari is a Professor in Electrical and Computer Engineering and Ohio Research Scholars Endowed Chair in Wide Area Surveillance at the University of Dayton,
Dayton, Ohio, USA. He is the Director of the Center of Excellence for Computer Vision and Wide Area Surveillance Research (Vision Lab) at UD. His research activities
include development of novel algorithms for human identification by face recognition, human action and activity recognition, brain signal analysis for emotion recognition and
brain machine interface, 3D scene creation from 2D video streams, 3D scene change detection, and automatic visibility improvement of images captured in various weather
conditions. He received his BS in electronics and communication engineering from the University of Kerala, India, and M Tech and PhD degrees in Electrical Engineering
from the Indian Institute of Technology, Madras. Prior to joining UD in February 2010, he worked as Professor in Electrical and Computer Engineering at Old Dominion
University, Norfolk, Virginia for 10 years. He worked at National University of Singapore during 1996-98 and led a research team for the development of a vision-guided
microrobotic endoscopy system. He also worked at Nanyang Technological University, Singapore during 1998-2000, and led the computer vision and image processing
related research activities in the Center for High Performance Embedded Systems at NTU. He holds three patents and has published more than 480 research papers,
including 80 peer-reviewed journal papers in the areas of image processing, pattern recognition, machine learning and high performance embedded systems. He has
supervised 20 PhD dissertations and 32 MS theses during the last 14 years. Currently 18 graduate students are working with him in different sponsored research projects.
He is participating in several federal and private funded research projects and he has so far managed around $15M research funding. He received several teaching,
research, advising and technical leadership awards. He is a Senior Member of IEEE and SPIE, and member of the IEEE Computational Intelligence Society. He is the coorganizer
of several SPIE and IEEE conferences and workshops.

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